Lossless adaptive encoding and decoding of integer data

ABSTRACT

A method and system of lossless compression of integer data using a novel backward-adaptive technique. The adaptive Run-Length and Golomb/Rice (RLGR) encoder and decoder (codec) and method switches between a Golomb/Rice (G/R) encoder mode only and using the G/R encoder combined with a Run-Length encoder. The backward-adaptive technique includes novel adaptation rules that adjust the encoder parameters after each encoded symbol. An encoder mode parameter and a G/R parameter are adapted. The encoding mode parameter controls whether the adaptive RLGR encoder and method uses Run-Length encoding and, if so, it is used. The G/R parameter is used in both modes to encode every input value (in the G/R only mode) or to encode the number or value after an incomplete run of zeros (in the RLGR mode). The adaptive RLGR codec and method also includes a decoder that can be precisely implemented based on the inverse of the encoder rules.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.10/978,157, entitled “Lossless Adaptive Encoding and Decoding of IntegerData,” filed Oct. 29, 2004, now allowed, the entire contents of whichare hereby incorporated by reference.

TECHNICAL FIELD

The present invention relates in general to the processing of digitaldata and more particularly to an improved method and system of losslessencoding and decoding of integer data using a novel backward-adaptivetechnique.

BACKGROUND OF THE INVENTION

Data compression is becoming increasingly important as the size ofcomputer data (such as text, audio, video, image and program files)continues to grow. Data compression is a way of encoding digital datainto an encoded representation that uses fewer bits than the originaldata. Representing the data in fewer bits means that the data occupiesless storage space and requires less transmission bandwidth.

In general, data compression compresses a data by predicting the mostfrequently-occurring data and storing it in less space. Specifically,data compression involves at least two different tasks: (1) defining adata model to predict the probabilities of the input data; and (2) usinga coder to generate codes from those probabilities. In addition, somedata compression techniques mathematically transform and quantize thedata to achieve even greater compression.

A compression technique may be lossless or lossy. A lossless compressiontechnique is reversible such that the original data before encoding andthe decompressed data after decoding are bit-for-bit identical. Lossycompression uses the fact there is much repetition in data that can bethrown away with much loss in quality. Lossy compression accepts theloss of some of the original data in order to achieve a highercompression.

Lossless compression typically is used to compress text or binary data,while lossy compression typically is used for audio, image and videodata. However, even lossy compression techniques can sometimes use alossless compression technique. For example, two commonly-used kinds ofcompression (or coding) technique are transform coding and predictivecoding. For such kinds of compression systems, the original data istransformed and then quantized (rounded to nearest integers), orpredicted based on (fixed or adaptive) signal models, and the predictionerrors (differences between the original and predicted data) are thenquantized. In both, cases, the data after quantization are in integerform. Once these integers are obtained, a lossless compression techniqueis used to encode the quantized values, in order to reduce the number ofbits needed to represent the data.

The set of these integer values usually has an associated probabilitydistribution function (PDF). These PDFs have a distribution such that ifthe data properties are well modeled by the predictor, in predictivecoding, then the prediction error should be close to zero most of thetime. Similarly, in transform coding, most of the quantized transformcoefficients are zero. FIG. 1 illustrates a typical probabilitydistribution for these integer values; zero is the most likely value,and the probabilities of nonzero values decrease nearly exponentiallyfast as the magnitude increases. The data has a probability distributionshown in FIG. 1 because the data that is being encoded using thelossless compression technique is not the original data. FIG. 1 is theinteger data resulting from quantizing transform coefficients orprediction errors.

Mathematically, the problem is to find an efficient solution to encodinga vector x containing N integers. Each of the elements x(n), n=0, 1, . .. , N−1, has a value according to a probability distribution similar tothat in FIG. 1, so that the most probable value is zero, and valuesfarther away from zero have fast decreasing probabilities.

A simple mathematical model for probability distributions like the onein FIG. 1 is the Laplacian, or two-sided geometric (TSG) distribution,characterized by a parameter θ: $\begin{matrix}{{P\left( {x,\theta} \right)} = {\frac{1 - \theta}{1 + \theta}\theta^{x}}} & (1)\end{matrix}$Note that the parameter θ controls the rate of decay in probability as|x| grows. The larger the value of θ, the faster the decay. Theparameter θ can be directly related to the probability that x=0, that isP(0, θ)=(1−θ)/(1+θ). Also, the expected magnitude of the source symbolis: $\begin{matrix}{{E\left\lbrack {x} \right\rbrack} = \frac{2\theta}{1 - \theta^{2}}} & (2)\end{matrix}$The entropy of the source is given in bits/symbol by $\begin{matrix}{{H(x)} = {{\log_{2}\left( \frac{1 + \theta}{1 - \theta} \right)} - {\frac{2\theta}{1 - \theta^{2}}{\log_{2}(\theta)}}}} & (3)\end{matrix}$Thus, a good encoder should map a vector of N values of x into abitstream containing not much more than N·H(x) bits, the theoreticalminimum.

The Laplacian distribution is a common model in media compressionsystems, for either prediction errors in predictive coders (like mostlossless audio and image coders) or for quantized transform coefficients(like most lossy audio, image, and video coders).

There have been many proposed encoders for sources with a Laplacian/TSGdistribution. A simple but efficient encoder is the Golomb-Rice encoder.First, the TSG source values x are mapped to nonnegative values u by thesimple invertible mapping: $\begin{matrix}{u = {{Q(x)} = \left\{ \begin{matrix}{{2x},} & {x \geq 0} \\{{{- 2}x} - 1} & {x < 0}\end{matrix} \right.}} & (4)\end{matrix}$that is equivalent to seeing u as the index to the reordered alphabet{0, −1, +1, −2, +2, . . . }. The new source u has a probabilitydistribution that approximates that of a geometric source, for whichGolomb codes are optimal, because they are Huffman codes for geometricsources, as long as the Golomb parameter is chosen appropriately.

An example of Golomb-Rice (G/R) codes is shown in Table 1 for severalvalues of the parameter m. It should be noted that when m equals a powerof two, a parameter k is used, which is related to m by m=2^(k). Themain advantage of G/R codes over Huffman codes is that the binarycodeword can be computed by a simple rule, for any input value. Thus, notables need to be stored. This is particularly useful for modernprocessors, for which reading from a memory location that stores a tableentry can take longer than executing several instructions. It is easy tosee that the parameter m determines how many consecutive codeword havethe same number of bits. That also indicates that computing the codewordinvolves computing u/m, where u is the input value. For most processors,an integer division takes many cycles, so the G/R code for general m isnot attractive. When m=2^(k) is chosen, which corresponds to a Ricecode, then the division u/m can be replaced by a shift, because u/m=u>>k(where >> denotes a right shift operator). Thus, computing the G/R codefor any input u is easy; simply compute p=u>>k and v=u−(p<<k). The codeis then formed by concatenating a string with p 1's with the k-bitbinary representation of v. TABLE 1 m = 1 m = 2 m = 4 m = 8 Input valuek = 0 k = 1 m = 3 k = 2 m = 5 . . . k = 3 0 0 00 00 000 000 0000 1 10 01010 001 001 0001 2 110 100 011 010 010 0010 3 1110 101 100 011 0110 00114 11110 1100 1010 1000 0111 0100 5 111110 1101 1011 1001 1000 0101 61111110 11100 1100 1010 1001 0110 7 11111110 11101 11010 1011 1010 01118 111111110 111100 11011 11000 10110 10000 9 1111111110 111101 1110011001 10111 10001 10 11111111110 1111100 111010 11010 11000 10010 11111111111110 1111101 111011 11011 11001 10011 12 1111111111110 11111100111100 111000 11010 10100 13 1111111111110 11111101 1111010 111001110110 10101 . . . . . . . . . . . . . . . . . .

It is clear from Table 1 that the choice of the G/R parameter k mustdepend on the statistics of the source. The slower the decay ofprobability as u increases, the larger k should be chosen. Otherwise,the codeword lengths grow too quickly. A simple rule for choosing k isthat the codeword length for a given input value u should approximatethe logarithm base 2 of the probability of occurrence of that value.

Although G/R codes are optimal for geometrically-distributed sources,they are not optimal for encoding symbols from a Laplacian/TSG sourcevia the mapping in Equation 4. This is because for an input variable xwith a TSG distribution, the variable u from Equation 4 has aprobability distribution that is close to but not exactly geometric. Inpractice, the performance is close enough to optimal (e.g. with a ratethat is typically less than 5% above the entropy), so G/R codes arequite popular. The optimal codes for TSG sources involve a set of fourcode variants, which are more complex to implement and improvecompression by 5% or less in most cases. Therefore, in most cases G/Rcoders provide the best tradeoff between performance and simplicity.

In FIG. 1, the probability distribution is represented by a singleparameter, which is the rate of decay of the exponential. The faster therate of decay, then the more likely is the value of zero. This meansthat in many cases zero is so likely that runs of zeros become verylikely. In other words, if the probability distribution rate of decay isfast enough then encoding runs is a good idea. Encoding runs of zerosmeans that just a few bits are used to take care of many entries in theinput data.

Encoding runs of data is efficiently performed using Run-Lengthencoding. Run length encoding is a simple form of data compression inwhich sequences of the same value repeated consecutively (or “runs”) arestored as a single data value and the length of the run, rather than asthe original run.

Prediction errors are much more likely to be zero if the data matchesthe model used by the predictor in predictive coding, for example. It ispossible, however, even with a good model, to every once in a while havea large value. This can occur when a boundary is reached, such as apixel value goes from a background value to a foreground value. Everynow and then big numbers can occur. When this happens, one type ofencoding technique that is more useful than Run-Length encoding is knownas a “Run-Length Golomb/Rice (RLGR)” encoding technique. One such RLFTencoding technique is disclosed in U.S. Pat. No. 6,771,828 to Malvarentitled “System and Method for Progressively Transform Coding DigitalData” and U.S. Pat. No. 6,477,280 to Malvar entitled “Lossless AdaptiveEncoding of Finite Alphabet Data”.

In reality, with the source of data varying, the probabilities will notstay constant and will vary over time. This is true with, for example,images and audio. Typically, these probability variations in the inputdata are handled in a variety of different ways. In JPEG, for examplethere is an entropy coder (a Huffman coder) whereby codewords ofdifferent lengths are used for different values to be encoded. TheHuffman table is usually pre-designed, that is, typically a number ofimages are obtained, their probabilities are measured, and an averagemodel is constructed that is used for all images. One problem with thisapproach is that with every portion of an image there is a loss inencoding efficiency, because the probability model being used by theentropy coder is good on average but not necessarily good for thatportion of the image.

From Table 1 it can be seen that there are two main issues withGolomb/Rice codes: (1) the probability decay parameter θ, or equivalentthe probability P(x=0) must be known, so the appropriate value of k canbe determined; and (2) if the decay parameter is too small, the entropyH(x) is less than 1, and thus the Golomb/Rice code is suboptimal, sinceits average codeword length cannot be less than 1 bit/symbol.

In practice, the first issue (estimation of the optimal Golomb/Riceparameter) is usually addressed by dividing the input vector into blocksof a predetermined length. For each block, the encoder makes two passesover the data. In the first pass, the average magnitude of input valuesis computed. For that, the parameter θ can be estimated from Equation 2,and the corresponding optimal k can be determined. In a second pass, theencoder generates the bitstream for the block by first outputting thevalue of k in binary form, followed by the concatenated strings ofGolomb/Rice codes for the data values within the block. This is theapproach used in essentially all lossless compression systems that useGolomb/Rice codes, such as JPEG-LS for lossless image compression,SHORTEN for lossless audio compression, and others. This is called a“blockwise adaptation” or “forward adaptation” model. The forwardadaptation model is forward in the sense that the encoder looks at thedata first before encoding, measures a statistical parameter (usuallythe average magnitude), and then encodes based on that parameter andputs the value of the parameter used to encode the data in a header, foruse by the decoder. Instead of trying to code the data all at once, thedata is broken up into small portions, or blocks. For each block, thestatistics of that block are measured, a statistical parameter ismeasure for that portion of data that matches what is in the buffer, andthe entropy coder is adjusted to that parameter. In the encoded file aheader is inserted that indicates the value of the parameter being usedto encode that block of data.

The second issue in practice, namely, encoding sources with very lowentropy, is usually addressed using a blockwise adaptation or forwardadaptation model, and if the average magnitude value of the inputsymbols in the block is small enough that the estimated entropy H(x) isless than 1, then the encoder uses Run-Length coding, instead ofGolomb/Rice coding.

Although these approaches work well in practice, they have two maindisadvantages. One disadvantage is that the encoder needs to read eachinput block twice, such that two passes are performed on the data: afirst time to compute the average magnitude to determine the Golomb/Riceparameter, and a second time to perform the actual encoding. Thisrequires the encoder to perform additional work and adds complexity. Insome applications encoding time is not an issue, but for digitalcameras, for example, it can slow down the encoding process or increasethe cost of random-access memory. In particular, the forward adaptationmodel must first look at the data and measure the statistics, find modelparameters, and then encode. This is not an issue if the encoder runs ona personal computer having a great deal of processing power. However, ifpictures taken with a cell phone, they are being encoded by the cellphone itself, where processing power is much more limited.

The second and most important disadvantage involves the difficulty inchoosing the block size. If the block size is too large, the statisticscould change dramatically within the block. On the other hand, if theblock size is too small, then the overhead of having to tell the decoderwhich parameter was used to encode that block of data becomesburdensome. For every block, the encoder must store what parametersvalues are being used to encode that block. At some point the overheadrequired to encode the small block is not worth the compressionachieved. This is creates a trade-off. On the one hand, if a small blockis used, the statistics of the block can be matched, however, measuringthe statistics is difficult because there are few numbers, and theoverhead of encoding is great. On the other hand, if a large block isused, the problem is that the statistics can vary greatly within theblock. In practice, it is hard to find a compromise between those twoconflicting factors, so that the block size is usually chosen to bebetween 128 and 2,048 samples, depending on the type of data to beencoded.

One solution is to use a backward-adaptive technique in the encoder.With backward adaptation, encoding starts with the decoder and encoderagreeing on initial states is for each block. In other words, eachparameter is initialized to a predetermined value, and then the encodingbegins. Every time the encoder produces an output symbol, that symbolcan be sent to the decoder immediately, because the decoder knows theparameter values used to encode it. After the encoder outputs a symbol,it then computes new values for the encoding parameters, depending onthe symbol that was output, according to a predetermined adaptationrule. The decoder knows the parameter adaptation rule, and therefore itcan also compute the new values for the encoding parameters. Thus, theencoding parameters are adjusted after every encoded symbol, and theencoder and decoder are always in sync, that is, the decoder tracks thechanges in the encoding parameters. This means that the encoder does notneed to send the decoder any overhead information in terms of whatparameter values were used to encode the data.

Therefore, what is needed is a lossless compression encoder and methodprovides efficient compression of integer data by switching betweenRun-Length encoding for encoding runs of zeros and Golomb/Rice encodingfor encoding numbers after the run of zeros. In addition, what is neededis an adaptive encoder and method that is capable of handling andencoding any input integer number that may appear after the run ofzeros. Moreover, what is also needed is an adaptive encoder and methodthat avoids the aforementioned problems with forward adaptation by usinga backward-adaptive technique to provide fast tracking and efficientcompression of the input data.

SUMMARY OF THE INVENTION

The invention disclosed herein includes an adaptive Run-LengthGolomb/Rice (RLGR) encoder and decoder (codec) and method for losslessencoding of integer data. The adaptive RLGR codec and method usesbackward adaptation to provide fast tracking of changing statistics inthe input data. Using backward adaptation, the adaptive RLGR codec andmethod quickly learns any changes in the statistics of the input data.In addition, the adaptive RLGR codec and method is capable of encodingany input integer value. The adaptive RLGR codec and method may beapplied in a wide variety of compression applications, includingmultimedia compression systems such as audio, image and video encoders.

The adaptive RLGR codec and method combines Run-Length encoding andGolomb/Rice (G/R) encoding in a novel way. In particular, the adaptiveRLGR codec and method has a first mode, which is G/R encoding only (G/Ronly mode), and a second mode, which is G/R encoding plus Run-Lengthencoding (RLGR mode). The adaptive RLGR codec and method also uses anovel backward-adaptive technique that adjusts the encoding parametersafter each encoded symbol. The encoding mode (G/R only mode or RLGRmode) is automatically determined from the parameter values. Noprobability tables or codeword tables are necessary, so the adaptiveRLGR codec and method can fit within a small memory footprint. Theadaptive RLGR codec and method thus is well-suited for modernprocessors, where memory access usually takes many more cycles thaninstruction fetching and execution. It is also well-suited for smalldevices with limited memory and limited processing power, because theadaptive RLGR codec and method does not need to buffer the input data inblocks, and does not need to process each data value twice.

The adaptive RLGR codec and method uses simple but novel combination ofcoding rules, mapping strings of the input data to output codewords,depending on the encoding mode. The adaptive RLGR codec and method alsouses a novel backward-adaptive technique having novel adaptation rules.Two parameters are used, namely, an encoder run parameter (s) and aGolomb/Rice (G/R) parameter (k). The encoding run parameter controlswhether the adaptive RLGR codec and method uses Run-Length encoding (fors>0) and, if so, then how Run-Length encoding is used. The G/R parameteris used in both encoding modes to either encode directly the input value(in the G/R only mode, s=0) or to encode the input value after anincomplete run of zeros (in the RLGR mode).

One of the main advantages of the adaptive RLGR codec and method is thatits parameters (s and k) are adjusted and updated after every codewordthat is generated. This allows any changes in the statistics of theinput data be tracked very quickly. No overhead is necessary to transmitthe encoding parameters to the decoder, because their changes aretracked by the decoder. Because the adaptation rules are simple, thecomputational complexity of using backward adaptation is low. Thus, theadaptive RLGR codec and method is attractive for many practicalapplications.

The adaptive RLGR method includes selecting between a first encodingmode that uses an entropy encoder other than a Run-Length encoder and asecond encoding mode that uses the entropy encoder combined with theRun-Length encoder, and adapting the encoders of the first and secondmodes using a backward-adaptive technique. Typically, the entropyencoder is a Golomb/Rice encoder. The backward-adaptive techniqueincludes using an encoding mode parameter, s, to select between thefirst and second modes, and a Golomb/Rice (G/R) parameter, k, for usewith the G/R encoder. If s=0, then the first encoding mode is selected,and if s>0, then the second encoding mode is selected. Each of theseparameters is updated after every codeword that is generated by theRun-Length encoder or the G/R encoder.

The encoding rule depends on the encoding mode. If s=0, then the nextinput value x is encoded by first mapping it to a nonnegative value uvia a simple 1−1-mapping rule (u=2 x if x>0, and u=−2 x−1, if x<0), andthen encoding u using a Golomb/Rice encoder with parameter k, so theoutput codeword is denoted as GR(u,k). If s>0, then a run r of zeros ofinput values x is first examined. If r=2^(s), then the run r is definedas a complete run and the output codeword is a single bit equal 0. Ifr<2^(s), then the run r is define as an incomplete run and the codewordis 1+bin(r,s)+GR(u,k), where bin(r,s) is the binary representation ofthe length of the incomplete run r in s bits, and GR(u,k) is theGolomb/Rice code for the value following the incomplete run r (usingagain the mapping from x to u).

After a symbol is encoded, then a parameter adaptation rules areapplied. The adaptation rules of the backward-adaptive technique includerules for each of the parameters s and k. The adaptation rule for s isfollows. If s=0 (G/R only mode), then the absolute value of the inputvalue x is examined. If |x|=0, then a scaled version of s, namely S, isincreased by a first integer constant, A1. If |x|>0, then S is decreasedby a second integer constant, B1. If s>0 (RLGR mode), then if the run ris a complete run, then S is increased by a third integer constant, A2.If the run r is an incomplete run, then s is decreased by a fourthinteger constant, B2. The value of s to be used for generating the nextcodeword is then computed as s=S/L, where L is a fixed parameter (thedivision by L is just a shift operator if L is chosen as a power of two.

The adaptation rule for k is as follows. From the input value u (recallthat the GR coder always operates on u values), a temporary value p iscomputed by p=u>>k (where >> denotes a right-shift operator). If p=0,then a scaled version of k, namely K, is decreased by a fifth integerconstant, B3. If p=1, then k is left unchanged. If p>1, then K isincreased by p. In this manner, the parameter k is updated for the G/Rencoder in both the first and second modes, after each codeword isgenerated. The value of k to be used for generating the next codeword isthen computed as k=K/L, where L is a fixed parameter (recall thatdivision by L is just a shift operator if L is chosen as a power oftwo).

It can be seen from the descriptions of the adaptation rules above thatthe adaptive RLGR method also includes a feature called “fractionaladaptation”. Fractional adaptation allows for a finer control of therate of adaptation. First, a scaling parameter, L, is defined, and thevalue of L is typically set to a power of two. Next, a scaled encodingmode parameter, S=s*L and a scaled G/R parameter, K=k*L, are defined.When using the above adaptation rules for s and k, the scaled parametervalues S and K are incremented or decremented by integer constants,depending on the generated codeword. After adaptation of S and K, thefinal parameter values s and k are computed by s=S/L and k=K/L. Thatway, the integer increments for S and K can be seen as fractionalincrements for s and k, which allow smoother control of the values of sand k, thus with better tracking of changes in the input statistics. Ifs and k were adjusted by integer increments after every encoded symbol,their values would fluctuate too much. Such noise in parameter valueswould lead to a decrease in the compression ratio (the ratio in thenumber of bits needed to store the input data in straight binary formatto the number of bits needed to store the encoded bitstream).

An adaptive RLGR codec includes modules for incorporating the adaptiveRLGR method described above.

An adaptive RLGR codec and method works by using decoding rulescorresponding to the encoding rules above, and using the same parameteradaptation rules described above. The decoding rule at the decoderreverses the previously described encoding rule at the encoder. Namely,if s=0, then the decoder reads as many bits from the input bitstream (orfile) as necessary, depending on the current value of the GR parameterk. In this manner, the decoder reads a complete codeword correspondingto a valid Golomb/Rice code GR(u,k), according to Table 1. Since theGolomb/Rice code is uniquely decodable for every parameter k, thedecoder then can decode that codeword. In other words, the decoder candetermine the value of the symbol u that was present at the encoder.From u, the decoder can determine the corresponding data value x simplyby using the inverse 1−1 mapping rule. In particular, if u is even, thenx=u/2, and, if u is odd, then x=−(u+1)/2. If s>0, then the decoder readsthe next bit from the input bitstream or file. If that bit equals 0(zero), then the decoder produces an output which is a string of rzeros, with r=2^(s). If that bit equals 1 (one), then the decoder readsthe next s bits as a binary representation of the variable r. Thedecoder then reads as many bits from the input bitstream (or file) asnecessary, depending on the current value of the GR parameter k. In thismanner, a complete codeword is read corresponding to a valid Golomb/Ricecode GR(u,k). From the G/R code the decoder can determine the temporaryvariable u and can compute the input data value x that followed thepartial string of zeros via the inverse 1−1 mapping rule. Specifically,if u is even, then x=u/2, and if u is odd, then x=−(u+1)/2. The decoderthen outputs the string of r zeros followed by that value x. Thedecoding process described above is performed to decode an inputcodeword into an output value or string of values that matches exactlywhat was seen at the encoder. Thus, the decoding process is lossless.

After decoding a codeword from the input bitstream or file as describedabove, the decoder then computes the same adaptation rules as describedfor the encoder above. In this manner, the decoder will adjust thevalues of the parameters s and k in exactly the same way as the encoderdoes. Thus, the parameters will have the correct value for decoding thenext bitstream (or file) codeword.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention can be further understood by reference to thefollowing description and attached drawings that illustrate aspects ofthe invention. Other features and advantages will be apparent from thefollowing detailed description of the invention, taken in conjunctionwith the accompanying drawings, which illustrate, by way of example, theprinciples of the present invention.

Referring now to the drawings in which like reference numbers representcorresponding parts throughout:

FIG. 1 illustrates a typical probability distribution for integer valuesthat works well with the adaptive Run-Length Golomb/Rice (RLGR) encoderand method disclosed herein.

FIG. 2A is a block diagram illustrating an exemplary implementation ofan encoder portion of the RLGR codec and method disclosed herein.

FIG. 2B is a block diagram illustrating an exemplary implementation of adecoder portion of the RLGR codec and method disclosed herein.

FIG. 3 illustrates an example of a suitable computing system environmentin which the adaptive RLGR codec and method shown in FIG. 2 may beimplemented.

FIG. 4 is a general block diagram illustrating components of theadaptive RLGR encoder shown in FIG. 2.

FIG. 5 is a general flow diagram illustrating the general operation ofthe adaptive RLGR encoder and method shown in FIGS. 2 and 4.

FIG. 6 is a flow diagram illustrating further details of the adaptiveRLGR encoder and method shown in FIG. 5.

FIG. 7 is a detailed flow diagram of the operation of the encoder modeselector of the adaptive RLGR encoder and method shown in FIG. 4.

FIG. 8 is a detailed flow diagram of the operation of the encoding modeparameter adaptation module of the adaptive RLGR encoder and methodshown in FIG. 4.

FIG. 9 is a detailed flow diagram of the operation of the Golomb/Rice(G/R) parameter adaptation module of the adaptive RLGR encoder andmethod shown in FIG. 4

FIG. 10 is a detailed flow diagram of the computation of the adaptationvalue used by the Golomb/Rice (G/R) parameter adaptation module shown inFIG. 9.

FIG. 11 is a working example illustrating the details of selecting anencoder mode for the adaptive RLGR encoder and method.

FIG. 12 is a working example illustrating the encoding details of theG/R only mode and the accompanying encoding mode parameter s adaptation.

FIG. 13 is a working example illustrating the encoding details of theRLGR mode and the accompanying encoding mode parameter s adaptation.

FIG. 14 is a working example illustrating the encoding details of theadaptive G/R only mode and as when as the G/R parameter k adaptationrules.

DETAILED DESCRIPTION OF THE INVENTION

In the following description of the invention, reference is made to theaccompanying drawings, which form a part thereof, and in which is shownby way of illustration a specific example whereby the invention may bepracticed. It is to be understood that other embodiments may be utilizedand structural changes may be made without departing from the scope ofthe present invention.

I. INTRODUCTION

The Run-Length Golomb/Rice (RLGR) encoder and method disclosed hereincan be used in a wide variety of compression applications. Multimediacompression systems (e.g. audio, image, and video codecs), as well asother systems that use a prediction or transform approach tocompression, usually generate integer data with probabilitydistributions similar to that of FIG. 1. The RLGR codec and method hasbeen implemented in applications for compression of image, audio,terrain elevation, and geometric map data. The results of using the RLGRcodec and method in these applications have been compression ratios thatare comparable to the most sophisticated entropy coders, but in asimpler implementation.

The RLGR codec and method disclosed herein is an improved technique forthe lossless compression of integer data. Vectors containing integervalues are mapped by the encoder into a bitstream, which later then canbe reconstructed exactly by the decoder. Using backward-adaptation forimproved performance, the RLGR codec and method quickly learns andadapts to changes in the statistics of the input data. The RLGR codecand method may be applied in a wide variety of compression applicationsincluding multimedia compression systems such as audio, image, and videocodecs,

The RLGR codec and method combines Run-Length encoding and Golomb-Riceencoding in a novel way, and is characterized by a backward-adaptationstrategy that adjusts the encoder parameters after each encoded symbol.Probability tables or codeword tables are unnecessary, which allows theRLGR codec and method to fit in a very small memory footprint. The RLGRcodec and method thus is particularly well-suited for modern processors,where memory access takes usually many more cycles than instructionfetching and execution.

One key advantage of the RLGR codec and method over previous kinds ofentropy coders is that its backward-adaptation strategy quickly learnschanges in the statistics of the data. Thus, in practice the RLGR codecand method has exhibited better performance than other kinds ofencoders, such as Huffman coders, block-adaptive Golomb/Rice encoders orcontext-adaptive arithmetic encoders. Another advantage of using abackward-adaptation strategy for the encoding parameters is thatprobability estimators are not needed. Still another advantage of theRLGR codec and method is that it performs adaptation after each encodedsymbol, in a single pass over the data, thus producing bettercompression results and faster encoding than encoders that use blockwiseor forward adaptation.

II. GENERAL OVERVIEW

FIGS. 2A & B are block diagrams illustrating an exemplary implementationof an adaptive Run-Length Golomb/Rice (RLGR) encoder and methoddisclosed herein. In FIG. 2A, a block diagram for an encoder portion ofthe adaptive Run-Length RLGR codec and method is shown. In FIG. 2B, ablock diagram for the decoder portion of the adaptive Run-Length RLGRcodec and method is shown. It should be noted that FIGS. 2A & B aremerely two of several ways in which the adaptive RLGR codec and methodmay implemented and used.

Referring to FIG. 2A, the adaptive RLGR encoder 200 runs on a firstcomputing device 210. The adaptive RLGR encoder 200 inputs and processesinteger data 220. In general, given the integer data 220, such as avector containing integer values, the adaptive RLGR encoder 200 encodesor maps the integer data 220 into an encoded bitstream 230. The integerdata 220 typically contains vectors of integers such that the mostprobable value is zero and any nonzero values have probabilities thatdecrease as the values increase. This type of integer data typically hasa probability distribution function (PDF) similar to that shown inFIG. 1. After the integer data is encoded, the encoded bitstream 230 maybe stored or transmitted.

Referring to FIG. 2B, a RLGR decoder 240 resides on a second computingdevice 250. It should be noted that although shown as separate computingdevices, the first computing device 210 and the second computing device250 may be the same computing device. In other words, the RLGR encoder200 and decoder 240 may reside on the same computing device. In general,the RLGR decoder 240 processes the encoder bitstream 230 and outputs areconstructed integer data 260. Because the adaptive RLGR encoder 200performs lossless encoding of the integer data 220, the RLGR decoder 240can read the encoded bitstream 230 and reconstruct exactly the originaldata vector contained in the integer data 220.

It should be noted that in practical applications a device or equipmentmay incorporate an RLGR encoder but not an RLGR decoder (for example, adigital camera). Similarly, a device or equipment may incorporate anRLGR decoder but not an RLGR encoder (for example, a digital audioplayer or a digital picture viewer).

III. EXEMPLARY OPERATING ENVIRONMENT

The adaptive Run-Length Golomb/Rice (RLGR) codec and method are designedto operate in a computing environment and on a computing device, such asthe first computing device 210 and the second computing device 250 shownin FIG. 2. The computing environment in which the adaptive RLGR codecand method operates will now be discussed. The following discussion isintended to provide a brief, general description of a suitable computingenvironment in which the adaptive Run-Length Golomb/Rice (RLGR) encoderand method may be implemented.

FIG. 3 illustrates an example of a suitable computing system environmentin which the RLGR codec and method shown in FIG. 2 may be implemented.The computing system environment 300 is only one example of a suitablecomputing environment and is not intended to suggest any limitation asto the scope of use or functionality of the invention. Neither shouldthe computing environment 300 be interpreted as having any dependency orrequirement relating to any one or combination of components illustratedin the exemplary operating environment 300.

The adaptive RLGR codec and method is operational with numerous othergeneral purpose or special purpose computing system environments orconfigurations. Examples of well known computing systems, environments,and/or configurations that may be suitable for use with the adaptiveRLGR codec and method include, but are not limited to, personalcomputers, server computers, hand-held, laptop or mobile computer orcommunications devices such as cell phones and PDA's, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputers, mainframe computers,distributed computing environments that include any of the above systemsor devices, and the like.

The adaptive RLGR codec and method may be described in the generalcontext of computer-executable instructions, such as program modules,being executed by a computer. Generally, program modules includeroutines, programs, objects, components, data structures, etc., thatperform particular tasks or implement particular abstract data types.The adaptive RLGR codec and method may also be practiced in distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed computing environment, program modules may be located inboth local and remote computer storage media including memory storagedevices. With reference to FIG. 3, an exemplary system for implementingthe adaptive RLGR codec and method includes a general-purpose computingdevice in the form of a computer 310. The computer 310 is an example ofthe first computing device 210 and the second computing device 250 shownin FIG. 2.

Components of the computer 310 may include, but are not limited to, aprocessing unit 320, a system memory 330, and a system bus 321 thatcouples various system components including the system memory to theprocessing unit 320. The system bus 321 may be any of several types ofbus structures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. By wayof example, and not limitation, such architectures include IndustryStandard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus,Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA)local bus, and Peripheral Component Interconnect (PCI) bus also known asMezzanine bus.

The computer 310 typically includes a variety of computer readablemedia. Computer readable media can be any available media that can beaccessed by the computer 310 and includes both volatile and nonvolatilemedia, removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media includes volatile andnonvolatile removable and non-removable media implemented in any methodor technology for storage of information such as computer readableinstructions, data structures, program modules or other data.

Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by the computer 310. Communication mediatypically embodies computer readable instructions, data structures,program modules or other data in a modulated data signal such as acarrier wave or other transport mechanism and includes any informationdelivery media.

Note that the term “modulated data signal” means a signal that has oneor more of its characteristics set or changed in such a manner as toencode information in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer readable media.

The system memory 330 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) 331and random access memory (RAM) 332. A basic input/output system 333(BIOS), containing the basic routines that help to transfer informationbetween elements within the computer 310, such as during start-up, istypically stored in ROM 331. RAM 332 typically contains data and/orprogram modules that are immediately accessible to and/or presentlybeing operated on by processing unit 320. By way of example, and notlimitation, FIG. 3 illustrates operating system 334, applicationprograms 335, other program modules 336, and program data 337.

The computer 310 may also include other removable/non-removable,volatile/nonvolatile computer storage media. By way of example only,FIG. 3 illustrates a hard disk drive 341 that reads from or writes tonon-removable, nonvolatile magnetic media, a magnetic disk drive 351that reads from or writes to a removable, nonvolatile magnetic disk 352,and an optical disk drive 355 that reads from or writes to a removable,nonvolatile optical disk 356 such as a CD ROM or other optical media.

Other removable/non-removable, volatile/nonvolatile computer storagemedia that can be used in the exemplary operating environment include,but are not limited to, magnetic tape cassettes, flash memory cards,digital versatile disks, digital video tape, solid state RAM, solidstate ROM, and the like. The hard disk drive 341 is typically connectedto the system bus 321 through a non-removable memory interface such asinterface 340, and magnetic disk drive 351 and optical disk drive 355are typically connected to the system bus 321 by a removable memoryinterface, such as interface 350.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 3, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 310. In FIG. 3, for example, hard disk drive 341 is illustratedas storing operating system 344, application programs 345, other programmodules 346, and program data 347. Note that these components can eitherbe the same as or different from operating system 334, applicationprograms 335, other program modules 336, and program data 337. Operatingsystem 344, application programs 345, other program modules 346, andprogram data 347 are given different numbers here to illustrate that, ata minimum, they are different copies. A user may enter commands andinformation into the computer 310 through input devices such as akeyboard 362 and pointing device 361, commonly referred to as a mouse,trackball or touch pad.

Other input devices (not shown) may include a microphone, joystick, gamepad, satellite dish, scanner, radio receiver, or a television orbroadcast video receiver, or the like. These and other input devices areoften connected to the processing unit 320 through a user inputinterface 360 that is coupled to the system bus 321, but may beconnected by other interface and bus structures, such as, for example, aparallel port, game port or a universal serial bus (USB). A monitor 391or other type of display device is also connected to the system bus 321via an interface, such as a video interface 390. In addition to themonitor, computers may also include other peripheral output devices suchas speakers 397 and printer 396, which may be connected through anoutput peripheral interface 395.

The computer 310 may operate in a networked environment using logicalconnections to one or more remote computers, such as a remote computer380. The remote computer 380 may be a personal computer, a server, arouter, a network PC, a peer device or other common network node, andtypically includes many or all of the elements described above relativeto the computer 310, although only a memory storage device 381 has beenillustrated in FIG. 3. The logical connections depicted in FIG. 3include a local area network (LAN) 371 and a wide area network (WAN)373, but may also include other networks. Such networking environmentsare commonplace in offices, enterprise-wide computer networks, intranetsand the Internet.

When used in a LAN networking environment, the computer 310 is connectedto the LAN 371 through a network interface or adapter 370. When used ina WAN networking environment, the computer 310 typically includes amodem 372 or other means for establishing communications over the WAN373, such as the Internet. The modem 372, which may be internal orexternal, may be connected to the system bus 321 via the user inputinterface 360, or other appropriate mechanism. In a networkedenvironment, program modules depicted relative to the computer 310, orportions thereof, may be stored in the remote memory storage device. Byway of example, and not limitation, FIG. 3 illustrates remoteapplication programs 385 as residing on memory device 381. It will beappreciated that the network connections shown are exemplary and othermeans of establishing a communications link between the computers may beused.

IV. SYSTEM COMPONENTS

FIG. 4 is a general block diagram illustrating components of theadaptive RLGR encoder 200 shown in FIG. 2. The adaptive RLGR encoderreceives as input an input value (or string of values) 400. An encodermode selector 405 then is used to select and switch between one of thetwo modes of the adaptive RLGR encoder 200. In particular, as shown inFIG. 4, the encoder mode selector 405 can select either a Golomb/Riceonly mode or a Run-Length and Golomb/Rice encoder mode. The Golomb/Riceonly mode uses only a Golomb/Rice encoder 410 having adaptiveparameters. The Run-Length and Golomb/Rice encoder mode uses both aRun-Length encoder 415 having adaptive parameters and the adaptiveGolomb/Rice encoder 410.

One of the above two modes is used to encode the input value (or string)400 to obtain a codeword 420. After the encoding of each input value (orstring) 400, the encoding parameters are adapted to track the statisticsof the input data. The adaptive RLGR encoder 200 uses at least two typesof parameters. First, an encoding mode parameter is used to controlwhether the encoding mode uses the Run-Length encoder and, if so, howthe Run-Length encoder is used. Second, a Golomb/Rice (G/R) parameter isused in both modes to: (a) encode every input value (if the encoder isin the G/R only mode; and (b) encode the number following an incompleterun of 0's (if the encoder is in the RLGR mode). These parameters andthe adaptation of these parameters will be discussed in detail below.

An encoding mode parameter adaptation module 430 is used to update theencoding mode parameter using a backward-adaptive technique. The module430 yields an update encoding mode parameter 435. Similarly, aGolomb/Rice parameter adaptation module 440 is used to update theoriginal G/R parameter using a backward-adaptive technique. This yieldsan updated G/R parameter 445. The adaptation of both the encoding modeparameter and the G/R parameter will be discussed in detail below. Oncethe parameters have been updated, a next input value 450 is processed bythe adaptive RLGR encoder 200 using the updated encoding mode parameter435 and the updated G/R parameter 445.

V. OPERATIONAL OVERVIEW

The operation of the adaptive RLGR encoder 200 and method used thereinas shown in FIGS. 2 and 4 now will be discussed. FIG. 5 is a generalflow diagram illustrating the general operation of the adaptive RLGRencoder 200 and method. The method begins by inputting digital data tobe encoded (box 500). In one tested embodiment, the input digital datais integer data in the form of a vector having elements that are integervalues. It should be noted that the each input digital data value can beany integer value, not restricted to a particular range (e.g. binary orbinary-plus-sign, as it is common in other entropy coders). Next, theencoder mode of the adaptive RLGR encoder is selected (box 510). Themode can either be the G/R only mode or the RL+G/R mode, depending onthe value of the encoding mode parameter. The selected encoder thenencodes the input digital data.

The digital data is encoded in the selected mode using encodingparameters that are initialized to certain values. However, because thestatistics of the input digital data may vary, the RLGR encoder 200 isadaptive. This adaptation allows the RLGR encoder 200 to track thestatistics of the input digital data and adapt to those statisticsquickly, to provide greater encoding efficiency. The RLGR encoder 200and method update the encoding parameters using a backward-adaptivetechnique (box 520). This updating of the encoding parameters occursafter each value or string of values of the input digital data isencoded. Moreover, the backward-adaptive technique includes noveladaptation rules, which are discussed in detail below. The encodeddigital data then is output (box 530). Next, the next value or string ofthe input digital data is processed using the method just described. Theupdated values of the encoding parameters are used in the encoding thenext input value or string. This process is repeated until all thedigital data has been encoded into an encoded bitstream.

FIG. 6 is a flow diagram illustrating further details of the adaptiveRLGR encoder and method shown in FIG. 5. Specifically, a value or stringof the digital data is received as input (box 600). Next, an encodermode is selected from one of the following modes: (a) a G/R only mode;and (b) a Run-Length and G/R mode (box 610). The input value or stringthen is encoded using the selected encoder mode (box 620).

After the input value or string has been encoded, the encodingparameters are updated. This adaptation process begins by definingfractional adaptation parameters (box 630). The fractional adaptationparameters are used to slow down the adaptation of the encodingparameters such that the optimal parameter values can be more closelytracked. The fractional adaptation parameters are discussed in moredetail below. Next, the fractional adaptation version of the encodingmode parameter is updated using a backward-adaptive technique containingnovel adaptation rules (box 640). Similarly, the G/R parameter isupdated using a backward-adaptive technique and novel adaptation rules(box 650). The encoded input value or string is appended to the encodedbitstream and the next value or string from the digital data to beencoded is input (box 660). The process begins again to encode the nextvalue or string using the updated encoding mode parameter and G/Rparameter.

VI. OPERATIONAL DETAILS

The operational details of the adaptive RLGR encoder 200 and method ofFIGS. 4, 5 and 6 discussed above will now be discussed.

Encoder Mode Selector

FIG. 7 is a detailed flow diagram of the operation of the encoder modeselector 405 of the adaptive RLGR encoder 200 and method shown in FIG.4. In general, the encoder mode selector 405 selects and switchesbetween two encoder modes based on the value of an encoding modeparameter. The selection of an encoder mode occurs after a value orstring of the digital data is encoded and the encoding parametersupdated.

The process begins by receiving as input an initial encoding modeparameter (box 705) and the input value (or string of zeros) to beencoded (box 710). A determination is made as to whether the value ofthe encoding mode parameter is equal to zero (box 715). If so, then theencoding mode is the G/R mode. Otherwise, the encoding mode is the RLGRmode.

In the G/R mode, the input value is encoded using the adaptive G/Rencoder (box 720). This is a G/R encoder that uses the backward-adaptiveadaptation rules for the G/R parameter. In the RLGR mode, which meansthat the encoding mode parameter is greater than zero, it can beexpected with a high probability that the input value contains a run ofzeros. A determination is made as to whether the run of zeros is acomplete or an incomplete run (box 725). A run is defined as a string ofthe same values adjacent each other. A complete run is defined as a runhaving a number of zeros equal to 2 raised to the power of the encodingmode parameter. For example, if the encoding mode parameter equals 3,then 23=8. Thus, in this example, a complete run is a run of 8 zeros.

If the run is a complete run, then the adaptive Run-Length encoder isused to encode the input value string as a codeword=0 (box 730). Inother words, a complete run is represented by a codeword of zero. In theabove example, a string of 8 zeros (which represents a complete string)would be represented by a codeword=0. If the run is an incomplete run,the input value string is encoded so as to tell the encoder that the runis incomplete, the number of zeros in the incomplete run, and the valueimmediately following the incomplete run. Specifically, the adaptiveRun-Length encoder is used to encode the input value string as anincomplete run, represented by a codeword=1 (box 735). This alerts thedecoder that the run is an incomplete run. Next, the length of theincomplete run is added to the encoded input value string (box 740).This encoded value represents the length of the incomplete run. Forexample, if the incomplete run is 4 zeros long, the value 4 (or “100” inbinary notation) is added to the encoded bitstream to tell the decoderthat the run length is equal to four.

The only piece of information left to encode is that value immediatelyfollowing the incomplete run. An adaptive G/R encoder is used to encodethe value immediately following the incomplete run (box 745). The G/Rparameter is used when using the adaptive G/R encoder. Finally, theencoded input value (if the encoding mode is the G/R mode) or theencoded input value string (if the encoding mode is the RLGR mode) isoutput (box 750).

Encoding Mode Parameter Adaptation Module

FIG. 8 is a detailed flow diagram of the operation of the encoding modeparameter adaptation module 430 of the adaptive RLGR encoder 200 andmethod shown in FIG. 4. In general, the encoding mode parameteradaptation module 430 updates an initial encoding mode parameter using abackward-adaptive technique. The update is performed after each value orstring of the digital data is encoded. The backward-adaptive techniqueuses novel adaptation rules to update the encoding mode parameter.

The process begins by receiving as input the initial encoding modeparameter (box 805) and the input value to be encoded (box 810). Adetermination then is made as to whether the encoding mode parameter isequal to zero (box 815). If so, then the encoding mode is the G/R onlymode, and otherwise the encoding mode is the RLGR mode.

For the G/R only mode, a determination then is made whether the absolutevalue of the input value is equal to zero (box 820). If so, then thecurrent scaled encoding mode parameter is increased by a first integerconstant (box 825). If not, then the current scaled encoding modeparameter is decreased by a second integer constant (box 830).

For the RLGR mode, a determination is made whether the run of zeros inthe string of the value is a complete or an incomplete run (box 835). Ifthe run is a complete run, then the current scaled encoding modeparameter is increased by a third integer constant (box 840). If the runis an incomplete run, then the current scaled encoding mode parameter isdecreased by a fourth integer constant (box 845).

Once the current scaled encoding mode parameter has been updated, thecurrent encoding mode parameter is replaced with the updated encodingmode parameter (box 850). This is obtained by dividing the scaledencoding mode parameter by a fixed scaling factor and keeping theinteger part of the result. Since the adaptation adjusts the scaledencoding mode parameter by integer steps, the actual encoding parameterbehaves as if it were adapted by fractional steps. This is called“fractional adaptation”, which permits finer control of the speed ofadaptation. The final updated encoding mode parameter then is output(box 855). Once again, the current scaled encoding mode parameter isupdated after every input value or string is encoded. The updatedencoding mode parameter then is used in the encoding of the next inputvalue or string.

Golomb/Rice (G/R) Parameter Adaptation Module

FIG. 9 is a detailed flow diagram of the operation of the Golomb/Rice(G/R) parameter adaptation module 440 of the adaptive RLGR encoder 200and method shown in FIG. 4. In general, the G/R parameter adaptationmodule 440 updates an initial G/R parameter using a backward-adaptivetechnique having novel adaptation rules. The update is performed aftereach value or string of the digital data is encoded.

The operation begins by receiving as input the initial G/R parameter(box 905) and an adaptation value (box 910), whose computation will bedescribed later. A determination then is made as to whether theadaptation value equals zero (box 915). If so, then the adaptation rulesare to decrease the scaled G/R parameter by a fifth integer constant(box 920).

If the adaptation value does not equal zero, a determination is madewhether the adaptation value is equal to one (box 925). If so, then theadaptation rules leave the scaled G/R parameter unchanged (box 930). Ifnot, then the adaptation rules are to increase the scaled G/R parameterby the adaptation value (box 935).

Once the G/R parameter has been adapted, the current G/R parameter isreplaced with the updated G/R parameter (box 940). This is obtained bydividing the scaled G/R mode parameter by a fixed scaling factor andkeeping the integer part of the result. Since the adaptation adjusts thescaled G/R mode parameter by integer steps, the actual G/R parameterbehaves as if it were adapted by fractional steps. Again, this is anexample of “fractional adaptation”, which permits finer control of thespeed of adaptation. Of course, if the G/R parameter is left unchanged(box 930) then there is no updating to perform, and the current G/Rparameter is the same. Finally, the updated G/R parameter is output (box945).

FIG. 10 is a detailed flow diagram of the operation of an adaptationvalue computation module 1000. Referring also to FIGS. 7 and 9, theadaptation value computation module 1000 produces the adaptation value(box 910) that is an input to the flow diagram in FIG. 9. The operationbegins by receiving two inputs, the current G/R parameter value (box1005) and the input value (box 1010) to the Golomb/Rice encoders (boxes720 and 745) of FIG. 7. Next, the input value is shifted to the right byas many places as the value of the G/R parameter (box 1020). Theresulting value is the adaptation value, which then is output (box1030).

VII. WORKING EXAMPLE

In order to more fully understand the adaptive RLGR encoder and methoddisclosed herein, the operational details of an exemplary workingexample are presented. It should be noted that this working example isonly one way in which the adaptive RLGR encoder and method may beimplemented.

The Run-Length Golomb/Rice (RLGR) encoder and method is an extension ofthe PTC entropy encoder disclosed in U.S. Pat. No. 6,477,280 citedabove. However, the PTC entropy encoder of U.S. Pat. No. 6,477,280 isused for encoding binary data (typically bit-planes of integer data).The RLGR encoder and method disclosed herein can encode integer datahaving any input value. In other words, the adaptive RLGR encoder andmethod disclosed herein can encode data of any alphabet. It should alsobe noted that while the RLGR encoder and method described herein is moreefficient when the data is likely to contain runs of zeros, it is atrivial matter to modify the encoding operations described above toaccount for runs of any other value that may be more likely to appear inruns.

One advantage of the RLGR encoder and method disclosed herein is that,unlike the PTC entropy encoder, there is no need to know the largestpossible number of the input data. Instead, the RLGR encoder and methodcan handle any size input value, no matter how large. This means thatthe RLGR encoder assumes that the input data has a Laplaciandistribution as shown in FIG. 1, and suddenly a large number appears inthe input data, the RLGR encoder and method is able to encode that largenumber. While more bits will be used to encode that large number than asmaller number, the large number will be encoded. However, the penaltyusing more bits will only be paid for that large number when it occurs,and not for every other value. This is due to the new mode selection andadaptation rules set forth below.

With the PTC entropy encoder the input data is received, broken into bitplanes, and then each bit plane is encoded with a G/R encoder. In theadaptive RLGR encoder and method disclosed herein, the G/R encoder isextended to the handle Laplacian data directly. This has the advantagethat the adaptive RLGR encoder and method uses single-pass encoding,which makes is significantly faster than the PTC entropy encoder.

The input data of the PTC entropy encoder had a Laplacian distribution,where small numbers are more likely. Sometimes small numbers are so muchmore likely that encoding runs of zeros is more efficient for aparticular part of the bitstream. However, the PTC entropy encoder wouldpick up the data, do one pass on the most significant bit plane, and goback and do one pass in the next bit plane. For example, if the data was16 bits, a pass was first done on bit #16 and encoded. Of course, mostof the data will be zero, because that bit only gets split for verylarge numbers, then keeps going down. As bits #5, 4, 3, 2, and 1 arereached these bits have lots of zeros and ones, which means that it getsto a point that encoding them does not help at all. Usually the leastsignificant bit is so random that a bit has to be used to encode thebit, that is, each input bit is directly copied to the output. Theproblem with the PTC entropy encoder is that encoding in bit planesrequires several passes at the data. In particular, the PTC entropyencoder has to encode the most significant bit, the next bit, then thenext bit, and so on. Clearly, this will take significantly more time,and in some cases the PTC entropy encoder is 1.5 to 3 times slower thanthe adaptive RLGR encoder and method disclosed herein.

Selecting the Encoder Mode

FIG. 11 is a working example illustrating the details of selecting anencoder mode for the adaptive RLGR encoder and method. The processstarts (box 1105) by reading an input vector having elements x(n), wheren=0, 1, . . . N−1 (box 1110). In this working example, the input vectorhas a probability distribution similar to the Laplacian distributionshown in FIG. 1. The probability distribution is represented by a singleparameter, which is the rate of decay of the exponential. The Laplaciandecay in shown in FIG. 1 goes down reasonably fast. In practice thedecay typically can be either faster or slower. The faster the rate ofdecay, then the more likely are values equal to zero.

The adaptive RLGR encoder and method is useful for the efficientencoding of integers. Specifically, this occurs when there is a vector(or buffer) full of integer numbers that need to be encoded efficiently.Typically, the integer data will be such that small numbers are muchmore likely than large numbers. And if the rate of decay of theprobability distribution of the data is high enough (meaning theexponential decays fast), this means that zero becomes even more likely.In this case encoding runs of zeros is a good idea.

In this working example, the input data has a probability distributionsimilar to the ideal shown in FIG. 1 because the data being encoded (orthe input data) is not the original multimedia data. For example, in thecase of an audio file, the adaptive RLGR encoder and method is notapplied directly to every audio sample. Similarly, in the case of apicture file, the adaptive RLGR encoder and method is not applied toevery pixel value. Instead, in many applications (including audio andimage coding) there is some model of the data. This means that somepredictors can be used that make reasonable guesses as to what the datashould be. Thus, prediction error can be the input data, and it is theprediction error that is encoded. Prediction error is the differencebetween the actual value and the predicted value. Moreover, predictionerrors are much more likely to be zero if the model for the data isreasonable. It is possible, however, even with a good model, to everyonce in a while have a large value. This can occur when a boundary isreached, such as when a pixel value goes from a background value to aforeground value. In other words, even with a probability distributionlike that shown in FIG. 1, every now and then big numbers can appear. Inother multimedia coding applications, the original data values (e.g.audio samples or pixels) are transformed via a transform operator, whosegoal is to concentrate the information in a small number of large-valuedtransform coefficients, and a large number of small-valued transformcoefficients. These coefficients are then quantized (rounded to nearestintegers). Thus, when all quantized transform coefficients are read as avector, the elements of that vector are more likely to contain smallvalues, and thus are quite amenable to compression via the adaptive RLGRencoder and method disclosed herein.

In addition, the adaptive RLGR encoder and method is lossless. Losslesscompression means that the input data is compressed in to a smaller sizeand then decoded to recover exactly the same values of the input data asthe original. In contrast, lossy compression does not recover exactlythe same value of original input data. Audio and video compression canuse lossy compression. However, in many of these lossy compressiontechniques, transforms or predictors are generated for the data. Thetransform or predictors then are quantized, meaning that they arerounded to the nearest integer. Once these integers are obtained,lossless compression frequently is used. This means that most lossycodecs for multimedia usually have a lossless encoder portion to encodethe integer values of the quantized prediction or transform operators.The adaptive RLGR encoder and method can be used to encode these integervalues. Usually, each of these integer values has an associatedprobability distribution function (PDF). These PDFs are similar to theprobability distribution of FIG. 1, in the sense that if the model isgood then the prediction error should be close to zero most of the time.

Referring to FIG. 11, next the encoding parameters {s, S} and {k, K} areinitialized (box 1115). The counter n and run then are set to zero (box1120). In this working example, the s is the encoding mode parameter, Sis the scaled encoding mode parameter, k is the G/R parameter, and K isthe scaled G/R parameter. In general, s controls the encoding mode andhow the Run-Length encoder is used, and k controls the G/R encoder. Theparameter s=S/L, where L is a scaling parameter. In this workingexample, L is equal to 16, which is a power of 2. This allows divisionto be performed by merely doing a shift.

The adaptive RLGR encoder and method has two distinct encoding modes. Afirst encoding mode performs Golomb/Rice (G/R) encoding only, withoutany Run-Length encoding. This G/R only mode is applied when zero is notthat much more likely in the data, such that there will not be runs ofzeros that frequently. The second encoding mode performs Run-Length plusG/R encoding. This RLGR encoding mode is used when runs of zeros are somuch more likely in the input data such that encoding the runs of zerossaves bits. Thus, there are two distinct encoding modes, and theadaptive RLGR encoder and method automatically selects and switchesbetween each of the encoding modes based on the value of s, the encodingmode parameter.

A determination is made whether s equal zero (box 1125). If so, then theencoding mode is the G/R only mode. In this case, the input data x(n) isencoded using the adaptive G/R encoder (box 1130). If s equals zero,then the encoding mode is the RLGR mode. In this case, the input datax(n) is encoded using both an adaptive Run-Length encoder and anadaptive G/R encoder (box 1135). Next, n is replaced by n+1 (box 1140)and a determination is made whether n is equal to N (box 1145). If not,then the process begins again with a new x(n) (point 1150). Otherwise,the output bitstream is written (box 1155) and the process is completedfor the given input data (box 1160).

Encoding Rules

The adaptive RLGR encoder and method uses novel encoding rules that arebased on the encoding mode parameter, s, and the G/R parameter, k. Table2 sets forth the coding rules for the adaptive RLGR encoder and methodfor mapping integer values x to a binary bitstream. As it can be seenfrom Table 2, code is controlled by parameters {s, k}. Parameter scontrols the main mode of operation (G/R only or Run-Length+GR), andparameter k controls the G/R coder. TABLE 2 s = 0 x ≧ 0: u = 2 |x| code= GR(u, k) “Golomb/ x < 0: u = 2 |x| − 1 Rice only” mode s > 0 Run of r= 2^(s) values x = 0. code = 0 “Run- Run of r < 2^(s) values x = 0, x >0: u = 2 |x| − 1 Length + followed by a value x ≠ 0. x < 0: u = 2 |x| −2 Golomb/ code = 1 + bin(r, s) + GR(u, k) Rice” mode

In this working example, a mapping value, u, is defined. The inputvalues, x, of the adaptive RLGR encoder and method can be positive ornegative. The input value x is mapped to a u value, where u is onlypositive. Thus, the signed input value, x, is converted into an unsignedequivalent representation, u. Equation 4 sets forth the mapping from xto u. In particular, the mapping says that 0 maps to 0, 1 maps to 1, −1maps to 2, 2 maps to 3, −2 maps to 4, and so forth, such that the uvalue is always positive. This is done so the G/R table (Table 1) can beused, because the G/R table is only for nonnegative values. This mappingallows the adaptive RLGR encoder and method to handle any inputalphabet. In other words, because the G/R table is used (which canhandle any input number), the input alphabet can be infinite and theadaptive RLGR encoder and method can handle any size number input. Theadaptive RLGR encoder and method is only limited by the size of thenumbers that the operating system can handle. It should be noted that inpractice the G/R encoding Table 1 does not need to be stored in memory.It is easy to see that the table entries have enough structure that thecodewords can be easily computed for any value of u and the encodingparameter k.

Given the u data, Table 2 states the code that goes in the channel aslong as the parameters s and k are known. The rules in Table 2 preciselydefine how the encoder encodes, which means that the decoder can use thesame rules in Table 2 to recover (or decode) the encoded data.

In the G/R only mode, which means that s=0, Table 2 states that themapping value u of the input value x is encoded using an adaptive G/Rencoder and the G/R encoding rule exemplified by Table 1. Thus, thecodeword used to encode x is based on the values of u and k. The G/Rparameter k is updated using a backward-adaptive technique, as discussedin detail below.

In the RLGR mode, which means that s>0, the input data is seen as a runof r values x=0. Looking at Table 2 for the RLGR mode, in one case(second row) the input data is a run of 2^(s) values of x=0. This isknown as a complete run. By way of example, assume that s=3. If theinput data is 2³=8 zeros in a row, then that run is encoded using therules of Table 2 as a single bit equal to 0. In other words, the entirerun of 8 zeros is mapped to a single bit whose value is 0. The decoderknows that if it reads a 0 it has to decode that into a string of 2^(s)zeros. In this situation, maximum compression is achieved. The overallcompression achieved by the invention is an average of this RLGR modeand the G/R mode.

If the input data has a run of less than 2^(s) zeros, then the run is anincomplete run. Referring to Table 2, third row, if the input data onlyhas, for example, 7 zeros in a row (or less than 2^(s)), the codewordcannot be 0, because 0 means 8 zeros in a row or a complete run. So thecodeword instead starts with a 1; this alerts the decoder that anincomplete run follows. Next, the codeword contains a binaryrepresentation of the length of the incomplete run. This binaryrepresentation uses s bits to specify the length of the incomplete run,r.

For example, assume that r=7, such that the incomplete run has 7 zeros.Then the codeword first starts with a “1” to let the decoder know thatan incomplete run follows. Since in this example s=3, the length of theincomplete run can only be a number between 0 and 7 (since it only takes3 bits to represent a number between 0 and 7). In other words, r cannottake more than s bits to be represented. So the codeword is appended toinclude 3 bits equal to 111 (binary 7). Now the decoder knows the run isan incomplete run and that the run length is 7 zeros.

The codeword next is appended to state what value comes immediatelyafter the incomplete run. This value that comes after the incomplete runis represented by a codeword from the adaptive G/R encoder. As shown inTable 2, first the value after the incomplete run is mapped by usingEquation 4. Then, the adaptive G/R encoder is used to obtain a G/R codethat is a function of {u,k}. The bottom of the lower right block ofTable 2 shows the entire codeword that is used to encode an incompleterun: code=1+bin(r,s)+GR(u,k), where bin(r,s) is the binaryrepresentation of the length of the incomplete run r represented by sbits, and GR(u,k) is the G/R code given by Table 1 that represents thevalue immediately following the incomplete run.

Fractional Adaptation

Fractional adaptation uses S and K instead of s and k. Fractionaladaptation is a way to slow down adaptation. It is possible to use theadaptive RLGR encoder and method without fractional adaptation. However,without fractional adaptation the adaptation usually changes too quicklyand typically fails to track the optimal parameters for the input datacorrectly.

In this working example, the adaptation of the s and k parameters isperformed using the scaled S and K parameters. Thus, S and K are updatedinstead of s and k. The relationship between s and S and k and K is asfollows: s=S/L and k=K/L, where L is a scaling parameter as explainedabove. Thus, when the adaptation is performed, the value of S is adaptedand S is divided by L to obtain the value of s. Note that all values areintegers, so by s=S/L it is meant the integer part of the result. Alsorecall that the fixed scaling parameter L is set to a value equal to apower of 2 (e.g. L=16), then division by L can be efficiently performedby shift operators.

Fractional adaptation is preferred because the adaptive RLGR encoder andmethod makes an adjustment of the parameters (s and k) for every codethat is generated. In other words, after a input value or string isencoded the adaptation rules are run. If s and k are adapted directlyvia integer-valued changes, then because they are integer numbers, allthat can be done is stay the same or increase or decrease by at least 1.However, suppose the input data is getting bigger, meaning that theparameters should be increased. If s is increased, say, by 1, whichcould be too much. For example, if s=3, then runs of 8 zeros are beingencoded. To increase s by 1, s would then equal 4, which means that nowruns of 16 zeros are being encoded via a single codeword=0. That jumpedfrom 8 to 16 could be a little large. When the next string is encoded,it would find s to be too big, and decrease s to 3. However, this may betoo small, and s would be oscillating.

Fractional adaptation allows a fractional increment of s. For example, scould be increased by 0.5 instead of 1. However, this is not allowed,because s is an integer parameter. So fractional adaptation performs aninteger increment in S, and divides S by L to give an fractionalincrement of s. For example, suppose that L=16, s=3, which means thatS=48. Assume that the input data becomes much smaller, so that S shouldbe increased. The next time S is increased from 48 to 52, when dividingby 16, s is still 3, but S is 52. If another small input data comes up,S could be increased from 52 to 64, and when S=64 then s=4. Thus,instead of bumping s to 4 immediately just because a smaller input datacame up, it took two times seeing a decrease in the value of the inputdata to go from s=3 to s=4. This ensures that there is no oscillation inthe parameters s and k.

Instead of using fractional adaptation it is possible to define a flagsuch that if there is a decrease or increase in the data then a certainnumber of encoding cycles passes before increasing or decreasing theparameters. A new parameter could be defined that keeps track of thenumber of encoding cycles. In other words, the parameter would keeptrack of how many times that the condition (input data increasing ordecreasing) should happen before the parameters are changed. It shouldbe noted, however, that this technique was tried, and the fractionaladaptation provided superior results.

Encoding Mode(s) Parameter Adaptation

The encoding mode parameter s is adapted after each input value orstring is encoded. When fractional adaptation is used, the fractionalencoding mode parameter S is actually adapted, instead of s directly.Table 3 sets forth the adaptation rules for the encoding mode parameters. After adaptation, the value of s is set to s=S/L, where L is thescaling parameter. In this working example, L was equal to a power oftwo such that division was be implemented by a right shift. In thisworking example, L=16. TABLE 3 s = 0 |x| = 0 : S

S + A1 “Golomb/Rice only” |x| > 0 : S

S − B1 mode s > 0 Run of r = 2^(s) values x = 0. S

S + A2 “Run-Length + Run of r < 2^(s) values x = 0, S

S − B2 Golomb/Rice” mode followed by a value x ≠ 0.

In Table 3 at the top, the rules for s adaptation are given when theencoder is in the G/R only mode. If the magnitude of the symbol to beencoded x equals 0, then the parameter s is increased by a first integerconstant called A1. The leftward pointing arrow (←) means that S isreplaced by S+A1. If the absolute value of x is greater than 0, then theparameter s is decreased by a second integer constant called B1. Theinteger constants A1 and B1 are increments and decrements that say howmuch s should be adjusted either up or down.

In Table 3 at the bottom, the rules for the adaptation of s are givenwhen the encoder in is the RLGR mode. If the run is a complete run,meaning that the run contains 2^(s) values of x=0, then the parameter sis increased by a third integer constant, A2. However, if the run is apartial run, meaning that the run contains less than 2^(s) number ofzeros, followed by a x value that is not zero, then the parameter s isdecreased by an fourth integer constant, B2. Using these adaptationrules, the parameter s (or S, if fractional adaptation is being used) isupdated after each input value or string is encoded.

By using the adaptation rules contained in Table 3, a probability modelfor the input data is being implicitly measured. In fact, instead offirst measuring probabilities and then trying to adapt the parameters,instead the parameters are being changed directly. In this workingexample, the following values were found to achieve excellent results:L=16, A1=3, B1=3, A2=4, B2=6 and B3=2. If appropriate values of A1, B1,A2 and B2 are used, then it has be observed that the adaptive RLGRencoder and method will actually track changes in the statistics of theinput data faster than the prior art. This is because the prior artfirst measures the whole statistical model and then adjusts parameters,such as the G/R parameter. The prior art does this because in order tocompute reasonable probability estimates a significant amount of data isneeded. The only data available are the values decoded currently and thevalues decoded before. This means to be precise in a probabilityestimate, a significant amount of data must be used before a probablyestimate can be made and a G/R parameter found. This means there will bea delay in the coding process. For example, if an audio file is beingencoded, then there is a delay in detecting any changes in the inputdata. This means that the G/R parameter is mismatched with the inputdata for a period of time. In other words, the adaptation rules setforth in Table 3 (for the s parameter) and in Table 4 below (for the kparameter) are much faster, and adapts to the statistics of the inputdata much quicker, than prior art methods. In fact, this is a bigadvantage over the prior art, namely, fast adaptation to the statisticsof the input data.

Golomb/Rice Only Mode

FIG. 12 is a working example illustrating the encoding details of theG/R only mode and the accompanying encoding mode parameter s adaptation.The process begins (box 1205) by reading the input vector x(n) (box1210). The two main processes of the G/R only mode are Symbol Encoding(box 1215) and Encoding Mode Parameter Adaptation (box 1220). The SymbolEncoding process begins by defining the mapping value u (box 1225), asshown in Table 2 for the G/R only mode. Next, the input value is encodedusing the adaptive G/R encoder (box 1230). It should be noted that theadaptive G/R encoder uses G/R parameter k adaptation, as shown in FIG.14. The Symbol Encoding process ends by appending the G/R code to theoutput encoding bitstream (box 1235).

The Encoding Mode Parameter Adaptation process begins by determiningwhether the value of u equals zero (box 1240). If not, then S is updatedand replaced by S increased by the first integer constant, A1 (box1245). If u is not equal to zero, then S is updated and replaced by Sdecreased by the second integer constant, B1 (box 1250). Finally, s isset equal to S divided by L (box 1255), and the process is finished (box1260).

RLGR Mode

FIG. 13 is a working example illustrating the encoding details of theRLGR mode and the accompanying encoding mode parameter s adaptation. Theprocess begins (box 1305) by reading the input vector x(n) (box 1310).Once again, the two main processes of the RLGR mode are Symbol Encoding(box 1315) and Encoding Mode Parameter Adaptation (box 1320). This time,however, the Symbol Encoding process uses an adaptive Run-Length encoderand an adaptive G/R encoder to encode the input value x(n).

The Symbol Encoding process begins by determining whether the value of xequals zero (box 1325). If so, then the run is replaced with run+1 (box1330). Next, a determination is made whether run=2^(s) (box 1335). Inother words, is the run a complete run or an incomplete run. If the runis an incomplete run, then the process is finished (point 1340). If therun is a complete run, then the bit=0 is appended to the bitstream (box1345). The value of run then is set equal to zero (box 1350).

If the value of x is not equal to zero (box 1325), then the bit=1 isappended to the bitstream (box 1355). Next, an s-bit binary value of run(or the length of the incomplete run represented by s bits) is appendedto the bitstream (box 1360). The value after the incomplete run then isencoded using the adaptive G/R encoder. In particular, the mapping valueu is defined (box 1365), the value is encoded using the adaptive G/Rencoder, and the G/R is appended to the bitstream (box 1370). Onceagain, it should be noted that the adaptive G/R encoder uses G/Rparameter k adaptation, as shown in FIG. 14. The Symbol Encoding processends by setting run equal to zero (box 1380).

The Encoding Mode Parameter Adaptation process updates and replaces Swith S increased by the third integer constant, A2 (box 1385), if thevalue of x is equal zero (box 1325). If the value of x is not equal tozero (box 1325), then S is updated and replaced with S decreased by thefourth integer constant, B2 (box 1390). Finally, the process sets sequal to S divided by L (box 1395) and the process finishes (box 1398).As mentioned before, division by L can be implemented via a shiftoperation, if the value of L is chosen as a power of two.

Golomb/Rice (k) Parameter Adaptation

The G/R parameter k is adapted after each input value or string isencoded. When fractional adaptation is used, the scaled G/R parameter Kis actually adapted, instead of k directly. Table 4 sets forth theadaptation rules for the G/R parameter k. After encoding a value u, theadaptation is controlled by the adaptation value of p=u>>k, meaning thatu is shifted to the right k places. After adaptation, the value of k isset to k=K/L, where L is a constant. In this working example, L=16.TABLE 4 p = 0 decrease K by setting K

K − B3 p = 1 no change p > 1 increase K by setting K

K + p

The G/R code from Table 1 depends on the parameter k. For example, ifthe value after an incomplete run is 13, the GR code for 13 is“11111111111110” (for k=0) and if k=1 it is “11111101”. The larger k is,the smaller the number representing 13 will be. And the smaller k is,the larger the number representing 13 will be. Thus, the parameter kmust be known. This means that the adaptive RLGR encoder and method cando a good job if it chooses a good value for s and for k. However, it isnot always advantageous to use large values of k because it will producelonger strings for smaller values of the input data, as shown inTable 1. In other words, a good choice for the value of k depends on theinput data. If the value is 13, then using a large value of k is a goodidea. However, suppose that the value after the incomplete run is “1”.Then, smaller value of k is desirable. Thus, for small values after theincomplete run, it is better to use a small k, and for large values itis better to use a large k. Thus, the choice of k is related to theprobability of the values. In the prior art there is a body oftheoretical work to this effect: the probability for the input data isknown (for example, if the input data is Laplacian where there is asingle parameter than controls the decay), there are well-known formulasthat from that decay parameter the parameter k to be used can becomputed. This gives on average the mapping to use as few bits aspossible.

Thus, it is important for the k parameter to be adaptive. That way, ifon the input data there are big values coming up, k should be increased,because for big values larger k is better. On the other hand, if thereare smaller values coming up, k should be decreased. Instinctively, itcan be seen that for big numbers k should be increased and for smallnumbers k should be decreased. Then as long as k is changed at a smallenough pace (such as when using fractional adaptation), the optimalparameters for the input data will always be tracked correctly.

The adaptation rules for k shown in Table 4 are significantly new. Inthe adaptive RLGR encoder and method, any value can come after theincomplete runs of zeros, so this value must be encoded. The encoding isdone using the adaptive G/R encoder and the G/R parameter k. Referringto Table 4, the input data is x. The input data x can be any integernumber, small x's are more likely (can be positive or can be negative).However, G/R encoding is only for positive numbers. A straightforwardmapping of x is used (see equation 4) to map x into u. The adaptation ofk is controlled by the adaptation value p, which is defined as u shiftedto the right k places. Thus, the adaptation value p is a scaled downversion of u. Or, equivalently, the p parameter is an integerapproximation to u/2^(k). Shifting k places to the right is equivalentto dividing the number by 2^(k). For example, if a number is shifted 5bits to the right this is the same as dividing the number by 32 (or 2⁵).The remainder is thrown away, and just the quotient is used.

Referring to Table 4, if the adaptation value p is equal to zero, then Kis updated and replaced by K decreased by the fifth integer constant,B3. If the adaptation value p is equal to one, then K is unchanged. Ifthe adaptation parameter p is greater than one, then K is updated andreplaced by K decreased by the adaptation value p.

If the adaptation value of p is equal to one, it means that the value ofu was close to 2^(k), and those are the kinds of values for which theparameter k is correct. Thus, as shown in Table 4, there is no change.If the value of the adaptation value p is 0, which means that the inputvalue was smaller than 2^(k). This means it is time to start decreasingk (because the input values are smaller than 2^(k)). The case where theadaptation value p is greater than 1 is much less likely because theinput values are not likely to be very big. But if the numbers are bigand p>1, then it is time to start increasing the k parameter.

Adaptive G/R Encoder

FIG. 14 is a working example illustrating the encoding details of theadaptive G/R encoding module, including the G/R parameter k adaptationrules. It should be noted that the k adaptation is used in both the G/Ronly mode and the RLGR mode but is not shown in FIGS. 12 and 13 to avoidcluttering the drawings. The process begins (box 1405) by reading theinput value u (box 1410). The two main processes of the adaptive G/Rmode are G/R Encoding (box 1415) and G/R Parameter Adaptation (box1420).

The G/R Encoding process begins by computing an adaptation value p and v(box 1425). The bitstream is appended with p bits equal to one (box1430). The k-bit binary value of v then is appended to the bitstream(box 1435). These operations comprise the Golomb/Rice encoder as definedin Table 1.

The G/R Parameter Adaptation process includes determining whether theadaptation value p is equal to one (box 1440). If so, then theadaptation value p is left unchanged (point 1445). Otherwise, anotherdetermination is made whether the adaptation value p equals zero (box1450). If not, then K is updated and replaced by K decreased by thefifth integer constant, B3 (box 1455). Otherwise, K is updated andreplaced by K increased by the adaptation value p (box 1460). Finally,the process sets k equal to K divided by L (box 1465) and the processfinishes (box 1470).

Results

The RLGR encoder and method of this working example has been implementedin applications for image, audio, and map data compression. The resultsof using the RLGR encoder and method in these applications have beencompression ratios that are comparable to the most sophisticated entropycoders, but in a simpler implementation.

In particular, with respect to existing entropy encoders for integerdata, the RLGR encoder and method achieves compression rates near thetheoretical maximum (dictated by the source entropy) for a large classof source symbol probability distributions, like the one in FIG. 1. Byway of example, well-known Golomb-Rice and Huffman encoders areefficient only for source entropies of 1 bit per symbol or higher.

VIII. DECODING

The adaptive RLGR codec and method also includes a decoder that can beprecisely implemented based on the encoder description above. Referringto FIG. 2B, a computing device (box 250) can implement just an RLGRdecoder 240. An adaptive RLGR decoder 240 and method receives codewordsfrom an encoded bitstream (box 230). Next, the adaptive RLGR decoder 240decodes the codewords by applying the reverse rules corresponding tothose in the G/R-only mode or the RLGR mode. Next, the encoding mode andG/R parameters are adapted using exactly the same rules as those for theencoder (described above). Finally, the decoded (reconstructed) integerdata is output (box 260).

Since the encoding rules are uniquely decodable and the adaptation rulesfor the decoder are identical to those of the encoder, the previousdescriptions of the encoding rule and adaptation rules also describeprecisely the operations of the decoder.

The foregoing description of the invention has been presented for thepurposes of illustration and description. It is not intended to beexhaustive or to limit the invention to the precise form disclosed. Manymodifications and variations are possible in light of the aboveteaching. It is intended that the scope of the invention be limited notby this detailed description of the invention, but rather by the claimsappended hereto.

1. A method for encoding digital data, comprising: a selection step forselecting between a first encoding mode and a second encoding mode toencode the digital data into an encoded bitstream, the first mode usinga first type of entropy encoder other than a Run-Length encoder and thesecond mode using the first type of entropy encoder combined with theRun-Length encoder; and an adaptation step for adapting the encoders ofthe first mode and the second mode based on the encoded bitstream usinga backward-adaptive technique.
 2. The method of claim 1, wherein thefirst type of entropy encoder is a Golomb/Rice encoder that combineswith the Run-Length encoder in the second mode to generate a Run-LengthGolomb/Rice (RLGR) encoder.
 3. The method of claim 2, further comprisingan encoding mode parameter step for using an encoding mode parameter toselect between the first and second modes.
 4. The method of claim 3,further comprising encoding in the first mode if the encoding modeparameter equals zero.
 5. The method of claim 4, further comprising afirst encoding mode parameter updating step for updating the encodingmode parameter, comprising: adding a first integer constant to theencoding mode parameter if the absolute value of the digital data isapproximately zero; and subtracting a second integer constant from theencoding mode parameter if the absolute value of the digital data isgreater than zero.
 6. The method of claim 2, further comprising a firstGolomb/Rice parameter updating step for updating a Golomb/Riceparameter, comprising: defining an adaptation value in terms of theGolomb/Rice parameter; decreasing the Golomb/Rice parameter by a thirdinteger constant if the adaptation value equals zero; leaving theGolomb/Rice parameter unchanged if the adaptation value equals one; andincreasing the Golomb/Rice parameter by the adaptation value if theadaptation value is greater than one.
 7. The method of claim 3, furthercomprising encoding in the second mode if the encoding mode parameter isgreater than zero.
 8. The method of claim 7, further comprising: a firstdefinition step for defining adjacent zero values in the digital data asa run; a second definition step for defining a complete run as a runhaving a number of zero values equal to 2 raised to the power of theencoding mode parameter; and a complete run encoding step for encodingthe complete run using the Run-Length encoder.
 9. The method of claim 8,further comprising representing the complete run as a codeword equal tozero.
 10. The method of claim 8, further comprising: a third definitionstep for defining an incomplete run as a run having a number of zerovalues equal to less than two raised to the power of the encoding modeparameter and followed by a value not equal to zero; an incomplete runencoding step for encoding the incomplete run by: placing a value of oneat the beginning of a codeword representing the incomplete run;representing a length of the incomplete run in the codeword; andrepresenting a value of the digital data following the incomplete runusing the Golomb/Rice encoder.
 11. The method of claim 8, furthercomprising a second encoding mode parameter updating step for updatingthe encoding mode parameter by adding a fourth integer constant to theencoding mode parameter.
 12. The method of claim 8, further comprising asecond Golomb/Rice parameter updating step for updating a Golomb/Riceparameter, comprising: defining an adaptation value in terms of theGolomb/Rice parameter; decreasing the Golomb/Rice parameter by a fifthinteger constant if the adaptation value equals zero; leaving theGolomb/Rice parameter unchanged if the adaptation value equals one; andincreasing the Golomb/Rice parameter by the adaptation value if theadaptation value is greater than one.
 13. The method of claim 10,further comprising a third encoding mode parameter updating step forupdating the encoding mode parameter by subtracting a fifth integerconstant from the encoding mode parameter.
 14. The method of claim 10,further comprising a third Golomb/Rice parameter updating step forupdating a Golomb/Rice parameter, comprising: defining an adaptationvalue in terms of the Golomb/Rice parameter; decreasing the Golomb/Riceparameter by a fifth integer constant if the adaptation value equalszero; leaving the Golomb/Rice parameter unchanged if the adaptationvalue equals one; and increasing the Golomb/Rice parameter by theadaptation value if the adaptation value is greater than one.
 15. Themethod of claim 2, further comprising adapting the encoders of the firstmode and the second mode after every codeword that is generated.
 16. Anadaptive encoder for encoding digital data, comprising: an encoder modeselector that selects between a first encoding mode and a secondencoding mode to encode the digital data into an encoded bitstream, thefirst mode using a first type of entropy encoder other than a Run-Lengthencoder and the second mode using the first type of entropy encodercombined with the Run-Length encoder; and an adaptation module thatadapts the encoders of the first mode and the second mode based on theencoded bitstream using a backward-adaptive technique.
 17. The adaptiveencoder of claim 16, wherein the first type of entropy encoder is aGolomb/Rice encoder that combines with the Run-Length encoder in thesecond mode to generate an adaptive Run-Length Golomb/Rice (RLGR)encoder.
 18. An adaptive Run-Length and Golomb/Rice (RLGR) encoder forencoding digital integer data, comprising: an encoder mode selector thatswitches between a first encoder mode performing Golomb/Rice (G/R)encoding only and a second encoder mode performing Run-Length encodingand G/R encoding; an encoding mode parameter, s, that controls which ofthe first and second encoder modes is used; and an encoding mode updatemodule that updates the encoding mode parameter, s, after each codewordis generated using a backward-adaptive technique.
 19. The adaptive RLGRencoder of claim 18, further comprising: a Golomb/Rice (G/R) parameter,k, that controls the G/R encoding of both the first and the secondencoding modes; and a Golomb/Rice parameter update module that updatesthe G/R parameter, k, after each codeword is generated using abackward-adaptive technique.
 20. The adaptive RLGR encoder of claim 19,wherein the encoder mode selector switches to the first encoder mode ifs=0 and switches to the to second encoder mode if s>0.